Jam City optimizes campaign performance and creative strategy with Singular

We got to sit down with Jam City’s VP of User Acquisition Marketing, Brian Sapp, to discuss how his team is using Singular to optimize campaign performance and improve their creative strategy. Watch our discussion on Jam City’s use of campaign analytics below!

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Introduction

My name is Brian Sapp. I’m the VP of UA (User Acquisition) Marketing at Jam CityJam City is one of the leading mobile casual gaming developers in the West. We have a fairly large portfolio of games. Some of our tentpole games are Cookie Jam and Panda Pop. But recently we launched Harry Potter Hogwarts Mystery, [and] that game has been a big success.

Connecting fragmented marketing data

Singular right now we use to ingest data from all of our ad networks and as you can imagine, when we spend with over 40 networks, that’s a lot of data. The manpower it would take to go into each network and pull in that API, do the work, or pull it in manually, would be extremely time-consuming.

Singular solves that for us, solves it in a much faster time, and more efficiently than we could do it ourselves. And then, having that data in the dashboard, especially for someone like me who’s spending across 8-10 titles, we have a massive portfolio, the dashboard really gives us the ability to easily pivot that data whether it be by spend, by channel, by paid installs, by tracker installs, impressions, it’s very, very useful, as well as creative. Having all of the data, especially creative data, plus images, plus the data behind the creative, in one dashboard is extremely valuable for us.

Aligning with Creative Product Marketing

So we actually have a team called Creative Product Marketing that focused on our creative roadmap/creative strategy and they’re using Singular to look at our performance by game, by channel, and right now it’s the fastest, easiest way we have to do that across all the different data sources.

Next-level Performance Analytics

Singular collects a lot of the ad network data for us and we’re using that to look at CPI by campaign, CPI by geo, paid performance, scale, spend, organics versus paid installs. And so we’re also ingesting data from our attribution partner which allows us to kind of marry the two, and so we get very granular on Singular’s reporting for whatever questions we have.

I use it for executive reports, building massive pivot charts, visuals that I want to see across the portfolio. The combination of our internal tools plus Singular really gives me everything I need.

Ready to take your growth marketing to the next level? Let’s connect!

How to scale user acquisition from $100 to $250,000/day

Have you ever scaled mobile user acquisition from $100/day to $250,000/day?

I’m guessing very, very, very few people can say yes to that question. Maybe fewer than 1,000 on the entire planet. So if you’re trying to grow — and grow aggressively — it makes a lot of sense to listen to someone who can answer with a yes.

Last week our CEO Gadi Eliashiv shared two articles by UserAcquisition.com’s Dave Riggs in one of Singular’s Slack channels. In them, Riggs talks from personal experience about the tech marketers need when they start scaling user acquisition for hyper-growth.

The most important point?

World-class measurement: something that our very best clients (and the best marketers in the world) whole-heartedly agree with.

The key differentiator between okay UA, good UA, and great UA comes down to data and measurement. Invest in it. The very best UA teams have sophisticated technology setups that allow them to slice and dice any data by any segment imaginable.

– Dave Riggs

That starts with an MMP, Riggs says: a mobile measurement platform. But it extends far beyond just an MMP. To do a world-class job of scaling user acquisition, you need five critical components in your marketing technology stack, he says.

  1. Attribution (an MMP)
  2. A cost data solution (could be same as #1)
  3. A database/data warehouse
  4. A BI platform
  5. A real-time visualization tool

Obviously, Singular plays in both slot #1 and slot #2. And Riggs has some high praise for Singular:

If you want a provider that offers both cost and attribution tracking, I recommend Singular. There’s nothing better.

– Dave Riggs

That’s pretty exciting for us to see here at Singular. But even more exciting are the reasons Riggs provides:

  1. Extreme accuracy
  2. “Far more network integrations than competitors”
  3. All your tracking in one place
  4. No gaps in reporting

Those are great reasons. Even greater, however, is how scaling user acquisition successfully at such extremes feels when you have the right tools: safe.

Let me repeat that: safe.

This is extremely important. After all, you’ve gone from $100/day, or $36,500/year to an almost unimaginable $250,000/day. If you kept your foot on the gas pedal all year at that rate — unlikely unless you’re achieving the most rarified heights of mobile success — that’s an astounding $91.25 million/year.

Some of Singular’s clients spend twice that. And more.

Feeling safe at such extremes of spending is almost more important than words can convey. (And actually being safe is even more critical.) Mistakes at this kind of spend velocity run into the millions of dollars very quickly.

So how do you scale your user acquisition spend 2,500 times? With the right technology … including Singular.

And how does that make you feel?

Picture it.

An expanse of open road appears before you. You accelerate and feel yourself pulled deeper into the plush leather. Your heart beats faster. Meanwhile, you appreciate the sweet complexity and design that holds you in perfect equilibrium, while the world around you flies past at breakneck speed. Thanks to world-class engineering, you know you’re perfectly safe, even as you accelerate. You’re blaring your horn and laughing like Cruella DeVil, as you ride up on the shoulder, leaving all the basic and intermediate fools in your dust.

– Dave Riggs

If you need that feeling today, let’s talk. Contact us, or request a demo. You deserve to feel safe. And if you need more info, here are the links to Dave’s articlesagain.

Nexon transforms marketing data into granular performance insights with Singular

We got the chance to catch up with Nexon‘s Director of Acquisition, Warren Woodward, and discuss how his team uses Singular to analyze and optimize the performance of their sophisticated user acquisition program. One topic we covered was how challenging it can be to analyze the performance of ad creatives, especially when advertising multiple titles across many ad partners. Thankfully, Nexon overcame this challenge. 😊Check out our discussion below!

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Introduction

My name is Warren Woodward, and I’m with Nexon. I’m the Director of User Acquisition. Nexon is an international gaming company. We have games for PC and mobile across the world. We have a couple of major IPs, such as Maple Story, and the biggest video game series in China, Dungeon and Fighter.

How Singular fits in Nexon’s growth stack

At Nexon, we leverage Singular in a few different ways. We use it as our source of truth. We also use it as a way to join data from multiple sources for day-to-day campaign optimization. Singular for us is a great way to look at all that data in one place. And for me as Director to look in on everything that everyone is doing and see it all in an apples-to-apples comparison to understand how to best move budget around.

Reaching growth goals

Singular helps us reach our growth goals ultimately because it saves us time in any number of ways. With the number of titles that we’re buying for, we don’t have the time to go into every separate source, every separate title, and look at it all à la carte. So the ways that we can slice and dice, and stack rank what’s important to us to see what’s giving us the highest ROI… everything across the game, where are we spending the most, what’s bleeding, let’s us judge how we can use a finite number of hours in the day to get the most impact to our campaigns.

Singular let me see it all in one place, slice and dice it. If I just want to see all of the performance for any given country or OS, or maybe just dive really deep to a particular ad network. It’s nice to have one dashboard that can quickly make all these ad-hoc reports. And also to do a creative analysis and collaborate with the creative team.

Perfecting storytelling

So one of the ways that we use this on a daily basis is to get our creative team talking about performance, the same way that we do; to be looking at a creative level across the dozens of creatives who might have any given time for a game. Understand the trends that are working. It helps us identify what we want to make next, what’s really working, and we start seeing a better return on investment for the creatives themselves when we can see how they’ve been performing in the ecosystem.

Ready to take your growth marketing to the next level? Let’s connect!

N3TWORK optimizes marketing spend and aligns internal teams with Singular [Video]

We recently had the privilege of sitting down with Nebojsa Radovic, Director of Performance Marketing at N3TWORK, to talk about how his team is leveraging Singular’s Marketing Intelligence Platform to optimize marketing spend and align their internal teams with a single source of truth for marketing performance. Check out our discussion below!

Transcription

Introduction

Hi, my name is Nebojsa Radovic, but most people in the industry know me as Nebo. I’m a Director of Performance Marketing at a company called N3TWORK, which is the developer behind Legendary Game of Heroes.

How Singular fits in N3TWORK’s growth stack

We’re currently using Singular pretty much as our marketing stack. We’re using [Singular] as both attribution and cost aggregation partner. We’re getting spend data from our ad partners and at the same time, we’re getting user-level data that tells us where the installs are coming from.

The importance of granular performance insights

Singular was very important for us simply because it unlocked certain opportunities that we were not having before. Think about optimizing at the publisher and creative level, combining those two, and trying to spend more dollars in the places where it makes sense. Without that data, it’s pretty much impossible to extrapolate manually and figure out what’s working and what’s not.

Let’s say if you’re trying to scale your spending from $100,000 a month to $10 million a month. You really need very granular, accurate data that comes in on time… When you’re spending a few thousand dollars a day, maybe you don’t need granular data. But if you’re trying to scale the spend across different channels and different geos you really need accurate and granular data to be able to do that. So thank you Singular!

Aligning internal teams with a single source of truth

Singular helped us a lot with aligning the Finance team with the User Acquisition team. And within the User Acquisition team, we have a media buying team, data analytics team and marketing creative [team]. All four teams, Finance and the three sub-teams of the marketing team are looking closely into the Singular data and trying to understand how to do their jobs better.

In particular, Finance is estimating what the spend is at the end of the month or week, whatever is that time period we’re looking at, and they’re estimating what the payback windows are going to be and the financial health of the company at a very high level. At the same time on the User Acquisition side, we’re just trying to make better buy decisions by using Singular data. This is crucial to do this job successfully.

Ready to take your growth marketing to the next level? Let’s connect!

To build or not to build: making build vs buy decisions for mobile attribution and aggregated campaign analytics (part 1)

Some of the larger marketing organizations we talk to in EMEA think about building aggregated campaign analytics and ROI insights themselves. They generally don’t see the full difficulty and continuous maintenance this project involves. In this article, I explore the challenges of building and why a solution like Singular meets and exceeds these needs. This is part one; part two will arrive in a month.

EMEA is a hub of marketers big and small representing every type of app developer and web-centric marketer you can think of. The data explosion has affected each one. It has made actionable insights, which make all the difference in this competitive landscape, the holy grail of every growth marketing team.

Build vs buy

One question that is a serious challenge for them all: should we build an in-house mobile attribution solution or buy it from a third party?

Our customers are smart and between them own over 50% of the top 100 grossing apps. So it’s no surprise that they employ intelligent engineers and data-savvy growth teams who already have the knowledge of how to achieve aggregated campaign analytics and could have a good shot at the greater challenge of getting ROI in an accurate, timely manner … although getting ROI at the most granular levels would be a massive challenge.

Therefore, it’s not a question of whether they can do it, but rather should they do it. We found that when addressing this question, the same considerations led even the largest enterprises out there to outsource this crucial work to a marketing intelligence platform like Singular.

The first thing to take into account is the cost of undertaking such a huge project and the time to completion.

Engineering time is not cheap and a company can rack up several hundred thousand dollars to build the required infrastructure even before considering the ongoing cost of maintenance. Not to mention that a project of this size and complexity will take months to complete and in such a fast-paced industry, this is long enough to start falling behind the competition.

Cost is not just measured in currency

However, the cost of this is not just monetary.

Valuable technical resources likely need to be diverted from core product projects, which impedes innovation and custom developments that address the specific needs of the business, allowing even more breathing room for competitors.

Getting the foundations right is no easy feat: you have to get a framework for your BI system, make sure that your MMP matches that framework, and then map your cost APIs into it correctly to get full aggregated campaign analytics. Furthermore, if your marketing efforts extend beyond Google and Facebook, you will have to set up multiple APIs with all the different networks you run with and for any new networks you want to test in the future.

If engineering time is limited, as it often is, and new networks are not integrated – what is the impact of the inability to test on the business? The cost of passing on new inventory and networks with new targeting and ad format capabilities cannot be underestimated.

Once you have your APIs connected, additional work is required to configure the internal dashboards to display the new data. It’s a manual process that is prone to human error which can easily render datasets inaccurate and therefore unsuitable for optimization purposes. If you’re going down the build route, you’ll need to put in place time and resources for checking accuracy before you even start thinking about which data visualization platform you’ll use to make sense of it all.

From aggregated campaign analytics to marketing intelligence platform

That’s another reason why our customers choose Singular, a marketing intelligence platform built with the modern growth marketer in mind, addressing their requirements of instant access to reliable data for granular optimization.

Even if all the above is accomplished so that data is flowing in and is accurate which we’ve seen can be done, the issue of combining it with internal data sets poses a true challenge.

Filling in the gaps and delivering the insights requires a complex infrastructure with strong identifiers for combining purposes to enrich campaign and publisher granularity, which almost certainly still leaves creative level combining — and therefore creative ROI — beyond reach.

All this means a lot of data and heavy queries that slow down the internal systems.

Our research and customer feedback reveals that the above challenges, opportunity cost, and continuous and expensive maintenance of self-built infrastructure are what drives small and big enterprises alike to a conclusion that a third party is a better solution for this essential need.

What you actually buy from Singular

Here at Singular, we understand these challenges well — after all, we went through the pain of building it ourselves.

Our product is our bread and butter and we’ve gone far beyond the basics to build a true marketing intelligence platform that frees up engineering time of our clients to build marvelous things that uniquely aid their goals while giving growth marketers the tools that they need.

What you buy from Singular is beyond the aggregation and standardization you’d expect to build yourself: you buy a world-class solution that is focused on continued innovation and automation, to give you unrivaled insights and optimization capabilities.

You buy teams that build and support integrations, improve infrastructure and system performance, and constantly work to add new features. You buy a data science team that make it their business to spot discrepancies, a support team that handles data flow errors and API issues, and a stellar (if I may so so myself) customer success team that makes sure the platform is serving your business.

If you had engineering and BI time to spare — what would you build?

See how DGN Games grew 85% and saved 15 hours each week with Singular.

Next month we’ll hear from an EMEA customer about how Singular has enabled their business and aided their growth strategy. If you have ambitious goals and are thinking of buying or building, reach out to us about a demo to see what Singular could do for you.

Introducing global-first Cross-Device, Cross-Platform ROI analytics

How do you grow ROI while maintaining CPA and scale?

This is a question marketers face every day. And answering this question has become more complex as they advertise on more platforms across more devices than ever before. When conversions happen, it’s a struggle to connect the dots and understand what caused them.

Back when Singular was founded in 2014, we focused on solving this challenge first for the complex, highly fragmented, mobile ecosystem: providing a single solution that automatically collects and combines spend data and conversion data to expose mobile marketing performance, including ROI, at unrivaled levels of granularity.

That is powerful. And we quickly became the de facto solution for unifying campaign analytics and mobile attribution to expose ROI.

But in 2019, the game is different

Top brands advertise over a wide range of platforms to users on multiple devices. A customer may see an advertisement for a product on her desktop, and later buy that product on her mobile app. With today’s analytics, it’s hard to connect the two experiences and measure the customer journey accurately.

For mobile-first brands, this often leads to two separate teams, one web, one mobile app, using different tools, and even different metrics, to measure the customer journey. For web-first brands, it results in limited investment in mobile apps, preventing them from diversifying their marketing efforts to bring in incremental users, leaving untapped growth potential on the line.

Moreover, inaccurate measurement leads to misguided decision-making. Matter of fact, poor data quality costs brands an average of $15 million annually, according to Gartner. Making an investment and creative decisions with inaccurate and incomplete datasets is just plain costly.

In true Singular spirit, we sought to solve this new challenge for our customers so they can drive growth more effectively and efficiently in this multichannel world. And I’m happy to say that we have leveraged our vast experience in attribution and marketing analytics to do just that.

Cross-device, cross-platform attribution

Today, Singular is announcing the first-ever cross-platform and cross-device ROI analytics solution for growth marketers.

With the release of Cross-Device Attribution, Singular’s Marketing Intelligence Platform connects marketing spend data to conversion results across devices and platforms. First, we ingest granular spend and marketing data from thousands of sources. Then we connect it with attribution data from our easy-to-implement in-app and web SDKs as well as direct integrations with customer data platforms, analytics solutions, and internal BI systems, bringing the full customer journey into a single view. Finally, we match the two datasets.

The result is the most accurate cohort ROI and CPA metrics available to marketers, at the deepest levels of granularity including campaign, publisher and even creative.

That’s ground-breaking. It’s revolutionary.

But bringing cross-device and cross-platform ROI into Singular and measuring it accurately, at granular levels, is only the beginning to driving impactful growth.

Granular data for growth

Marketers can now access granular ROI cohort reporting that is more accurate than ever, as you can get clear, combined revenue for users across all devices. This is critical to achieving profitable growth and only possible with Singular – a complete platform that innovates beyond a single attribution solution.

Moreover, marketers can also utilize the wide set of capabilities that Singular’s Marketing Intelligence Platform offers to make smarter decisions and optimize their growth efforts with additional cross-device visibility; plus, they have more visibility into essential context such as the exact creative customers engaged with and the audience segments they belong to.

For example, you may find that a web channel’s impact is much higher than expected for specific types of customers. And now you can analyze the impact of the same creative across mobile and web.

In fact, we won’t be surprised if marketers start shifting investments with this new level of clarity. We are excited to see how growth strategists are going to rise above the crowd using this new solution to become part of the future wave of sophisticated marketers. Gone are the days of attribution feature wars – Marketing Intelligence has arrived.

Launching Cross-Device Attribution is just another step towards achieving our goal: to be every marketer’s indispensable tool in driving growth. We keep working not only to ensure that you can innovate your growth processes and have access to the highest data accuracy but also to ensure that we bring you the right insights at the right time to help you make timely strategic and operational decisions.

Are you ready to take part in the future of growth?

Find out what Singular can do for you

200 CMOs on marketing data: ‘Actionable insights’ are top priority for 2019, followed by consumer privacy

What do brands need most out of their marketing data in 2019?

Actionable insights, consumer privacy protection, and full marketing data unification, chief marketing officers say.

I recently asked 199 CMOs, VPs of marketing, and other marketing leaders what their biggest challenges for marketing data will be in 2019 for a story in Inc. (There was far too much to write there, so this post became necessary.) Tops on marketers’ lists of priorities? Actionable insights in an avalanche of data. But just behind it in today’s climate of consumer privacy breaches was privacy – and trust.

Here’s how Felicity Carson, CMO for IBM’s Watson division, put it:

“Among all the marketing data challenges, the biggest in 2019 will be how marketers instill trust in data – both for the marketing discipline and customers – balanced with the need to improve customer experience.”

The 800-lb gorilla in the room?

Marketers have far too much data already. That’s a consumer privacy risk, but it’s also a potential marketing intelligence nightmare.

“Marketers are drowning in data from various analytics systems,’ says Jo Ann Sanders, VP of Product Marketing at Optimizely. “What marketers are going to have to do going forward … is to go beyond analytics data … and adopt new, agile test and learn practices.”

Marketers don’t need more data.

What they need are actionable insights drawn from the data they already have. Marketers’ third priority, unifying all their marketing data, will help.

“With the exponential growth of data over the past decade and into the new year, it’s becoming harder daily to turn information into action,” says SurveyMonkey CMO Leela Srinivasan. “While more data has the potential to deliver more meaningful insights, prioritizing an action plan to address it is critical.”

Consumer privacy and data security

Insights are essential for growth, that’s clear.

But a strong brand untainted by consumer privacy breaches is also essential for growth. Anyone who feels otherwise, just ask any company that experienced a privacy breach in 2018 … and look at its stock price impact.

That’s why, almost shockingly, marketers’ second-biggest concern has now become consumer privacy, the security of consumer data that brands now possess, and regaining the trust of their customers.

“The single biggest challenge B2B marketers face in the coming year will be balancing privacy and personalization to regain the trust of their audiences,” says Penny Wilson, CMO of social media marketing platform Hootsuite. “That starts with respecting [consumers’] privacy, being open and transparent about when and why data is collected, and then leveraging the data that customers are willing to share to create personalized one-to-one experiences that deliver unique value.”

This requires a massive change in data collection policy.

“Going forward, brands must focus less on maximizing reach, and more on generating transparent, quality engagements that add value to their customers,” Wilson adds.

This is not business as usual for marketers and advertisers, who have typically wanted as much data as possible. In fact, a new “social contract” between brands and consumers will become so important, says Lloyd Adams, SVP at SAP North America, that data ethics will become more important than data analytics.

Unifying marketing data: a top-3 priority

What else do marketers care about?

Not far behind privacy/security/trust, marketers rank unifying marketing data as a top-three priority.

The challenge is obvious.

In a universe of 7,000 marketing and advertising technology tools, marketers are both doing and learning so much more from their prospects and customers. But most of those actions and insights are being generated in siloed, dispersed systems.

“The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration,” says Tim Minahan, CMO of Citrix. “Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.”

The result is not pretty.

“Everyone’s data is a mess,” says Peter Reinhardt, CEO of Segment.

Identifying insights from your marketing data and then unifying them for a single view of customers – and a unified understanding of marketing success – is critical to cleaning it up.

“Data lives in different places — sales, customer service, digital marketing,” says Selligent Marketing Cloud CEO John Hernandez. “The biggest data-related challenge [for 2019] will be consolidation and a full 360-degree view of the customer relationship.”

That’s a difficult challenge, Hernandez says, and CMOs agree.

And in fact, not only is it hard right now … it’s getting harder.

“The biggest issue with marketing data is federating it into a meaningful whole picture,” says Eric Quanstrom, CMO of Cience. “As CMO, I live in (literally) a dozen different dashboards, daily. And that number is growing.”

Marketing data used to be fairly simple: survey data, market data, customer data, product use data, and probabalistic reporting on ad performance in traditional channels. New digital channels offer deterministic reporting possibilities, but with web and mobile and apps and wearables and IoT – to say nothing of platform proliferation like email and social and messaging and search – it’s getting harder.

And all of that proliferation leads to siloed data sets.

The problem with siloed data is that fragmentation subverts complementarity, says Rebecca Mahoney, CMO at MiQ. When data isn’t complementary and doesn’t add up to a complete picture, the marketing results is an inability to detect new opportunities, or see weak links in existing marketing campaigns, she says.

Data lakes may not save marketers, says Daniel Jaye, founder of Aqfer, a data lake provider.

In fact, they can actually exacerbate the problem because most data lakes inevitably become data swamps, Jaye says. Widespread data proliferation, chaotic file partitioning and sharding practices, and the lack of traditional data management tools all cost marketers the opportunity to achieve integrated insights.

Marketing intelligence unifies data insights

But there is hope.

Good marketing data practice does result in growth.

“With a holistic view of data, powered by marketing intelligence, campaign performance will drastically improve, and otherwise unidentified business opportunities will become unlocked,” Mahoney says.

It’s true that not every marketer will have a single marketing cloud for all their marketing technology and data needs. And even most marketing cloud customers also use additional tools to engage and understand their customers.

That suggest that centralization of marketing insights, particularly on paid but also on organic marketing efforts, is what will help marketers the most. Engagement happens where the customers are and data lives in the tools a brand uses to connect with them. Marketing intelligence aggregates then insights from the entire gamut of customer engagement into one single unified view.

(Learn more about that here.)

Other top concerns: quality, quantity, and AI

Marketers are also concerned about the quality of the data that they have, and its accuracy. 13% said that accuracy was a top concern in 2019. Another 12% said they have too much data.

“In many ways, marketing has too much data on its hands,” says David Meiselman, the CMO of corporate catering company exCater.

As Citrix CMO Tim Minahan said above, we’ve put too many tools in place to collect and analyze marketing data. The result is frustration.

A potential savior?

Artificial intelligence.

“We … believe marketing and customer engagement will be an excellent first use-case for enterprise AI,” says Patricia Nagle, CMO at OpenText. “AI systems can analyse structured and unstructured data to identify opportunities for marketing outreach, customer support, and other actions that enhance overall customer experience.”

That’s true, and AI is a tool that marketing is already seeing results from in fraud reduction, creative reporting, and other areas.

But it does some with some dangers as well.

“Deep learning models have been shown to be vulnerable to imperceptible perturbations in data, that dupe models into making wrong predictions or classifications,” says Prasad Chalasani, Chief Scientist at MediaMath. “With the growing reliance on large datasets, AI systems will need to guard against such attacks on data, and the savviest advertisers will increasingly look into adversarial ML techniques to train models to be robust against such attacks.”

And finally … all the other quotes

When you ask 200 top marketers for their insights, you get a lot of insights. And they’re too good to bury.

So here are many of the additional quotes that marketers provided, broken down into categories that I’ve chosen. Some of them are partially referenced above, but are given in complete form here. Each of the responses is answering a simple question:

What are brands’ biggest challenges with marketing data in 2019?

Marketers need: Data accuracy and quality

Peter Reinhardt, co-founder and CEO of Segment

The biggest challenge for marketing data in 2019 will be data correctness. Everyone’s data is a mess. Consumers are bombarded with tons of noise, much of it based on wrong data, names, and locations. As a result, customers are burned out. It doesn’t matter how much a company invests in personalization if the underlying data is incorrect. For businesses to truly succeed in 2019 and beyond, they need to prioritize making sure their data is clean and accurate.

Martha E Krejci, The Tribefinder

The biggest challenge with marketing data in 2019 will be determining how good the data really is. Before this rise in cookie awareness people weren’t really flushing the cookies or clearing their cache as much, which lent itself to long-standing good demographic data. Now, the data isn’t as deep, therefore not as reliable. In 2019, businesses will need to learn to re-target.

Joanne Chaewon Kim, Junggglex

Not surprisingly, war against fraud will be the biggest challenges mobile marketers will have to face. In addition to common fraud cases like SDK spoofing and click spamming, more and more new types of fraud will stop developers from obtaining real users. Our job as mobile marketers is to keep educating ourselves about different types of fraud and the pattern of each fraud cases, so that we can take a proper action when we find them.

Marketers need: Actionable insights and marketing intelligence

Mark Kirschner, CMO, Albert

The best tools solve the disconnect between data, insight, and action, incorporating multiple sources of data to execute, allocate, attribute and optimize digital campaigns across channels.

Tara Hunt, CEO + Partner, Truly

Marketing data still struggles with insights and it would be amazing to see more of a focus on this essential craft. There are endless tools for gathering the WHAT – numbers and histories and basic information about your customers – but very little that helps us figure out the WHY. The big challenge in 2019 (and likely for a few more years) is going to be training people to understand how to read the what to get to that why.

Phil Gerbyshak, Digital Selling and Marketing Strategist

With all the data collected, the biggest challenge with all the marketing data is finding the most meaningful data, and then figuring out the most actionable insight from that meaningful data. Too often reports for reports sake are created, even with AI to help us find the patterns. Taking the time to think about what you want to accomplish and setting up your data accordingly will challenge marketers and delight stakeholders in 2019 and beyond.

David Berkowitz, Principal, Serial Marketer

There is so much data out there that ‘big data’ is no longer the priority; there is a need for actionable data that means something to marketers. The other challenge is that the biggest winners on the platform side are increasingly closed and stingy with their access, which may be necessary for consumers and benefits the platforms but hurts marketers. Finally, marketers will have to grapple with a savvier base of consumers who are constantly reading mainstream press coverage about data abuses; marketers will need to determine how cautious they want to be with collecting and accessing consumer information.

Douglas Karr, CEO, DKnewmedia

What is the biggest data challenge for marketers in 2019?

Building actionable results based off of accurate data. We continue to see an inability of our clients to properly read analytics and come to assumptions. I hope continued AI and machine learning will add tools to assist.

Felicity Carson, CMO, IBM Watson Customer Engagement

Among all the marketing data challenges, the biggest in 2019 will be how marketers instill trust in data – both for the marketing discipline and customers – balanced with the need to improve customer experience by identifying meaningful patterns buried deep within the deluge of data. Compounding this challenge is the need to break down compartmentalized martech and adtech stacks that house this information, coupled with the need to have contextualized understanding of aggregated customer data across the organization such as commerce and digital teams. Marketing teams will need to rely on AI to achieve this level of high performance at scale, particularly in the new era of the ‘Emotion Economy’ that requires organizations to engage with customers in relevant ways on issues that personally matter to them.

Julie Huval, Beck Technology

The biggest challenge with marketing data in 2019 will be to decipher which of the outlier [datapoints] are leading indicators into new market growth.

Leela Srinivasan, CMO of SurveyMonkey

Today, we have access to more data than ever before, but with the exponential growth of data over the past decade and into the new year, it’s becoming harder daily to turn information into action. A study by IDC bleakly projects that by the end of 2025 only 15% of global data will be tagged; of that, only 20% will be analyzed and approximately 6% will be useful.

While more data has the potential to deliver more meaningful insights, prioritizing an action plan to address it is critical. In 2019, B2B marketers will be laser-focused on finding a way to cut through the massive troves of data available and identify the insights that matter most.

Christina Warner, Walgreens Boots Alliance

The biggest challenge with marketing data is the ability to find the useful insights to create concrete actionable next steps. We have so much data, but not enough of an efficient way to sift through the noise accurately for truly useful data.

Lauren Collalto-Rieske, CMO, Contap Social

The biggest challenges we have as a startup are: having easy-to-use data that doesn’t require a ton of training like a Nielsen or IRI platform and being able to triangulate all of our data among a two-person team. Right now, we are using about 8 different vendors to analyze one or more stages within the customer lifecycle, and while it’s great to have all of this data, it’s not easy to triangulate it. It would be great to have 1 platform that could assess all or most of our marketing program’s performance, but those platforms usually come with a large price tag that we can’t afford.

Moshe Vaknin, CEO and CO-Founder, YouAppi

One of the biggest challenges marketers will face in 2019 is how to better analyze consumer behavior and turn those insights into effective marketing. Consumers spend 40 hours a month and three hours a day in apps, mobile time spent will surpass time spent in TV in 2019, so marketers need to change their traditional planning behaviors for this brave new world. They must integrate their traditional teams with their digital teams, combine their video teams into one cohesive team, and integrate the data across all channels so that they can be smarter about how they find their most valuable customers. It is also getting harder with privacy, however, companies with strong technology especially predictive algorithms can predict users intentions based on less data. We are just scratching the surface on data analysis and with new data privacy laws, this challenge will only get harder.

Tim Minahan, Chief Marketing Officer and SVP, Citrix

Every marketing challenge can be whittled down to a mathematical equation – whether it’s measuring customer sentiment, tracking conversions, or weighing the return on a particular campaign. Data-driven marketing can eliminate much of the he-said/she-said friction that has historically muddied sales and marketing relationships. It can cut through emotional biases and drive the right course of action to reach and win the market and deliver the best results. The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration. Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.

To tackle this problem, marketing organizations need to tap into intelligent technologies like machine learning that can make data-driven marketing smarter and easier to execute. Machines can recognize patterns and analyze things with greater speed and efficiency and automatically deliver insights and intelligence that humans can use to make more informed decisions and engage customers and prospects in the most optimal way. And beyond tools that automate tasks and make marketing more efficient, we need to equip our teams with solutions that enable them to push the envelope. Like using artificial intelligence and machine learning to see data in new and innovative ways. Or leveraging augmented reality to create entirely new worlds where we can interact with customers in insanely personal ways.

Julia Stead, VP of Marketing, Invoca

As marketing tools and automated solutions continue to flood the market, the biggest challenge marketers will face is applying data to create timely, emotionally-reciprocal experiences. More and more consumers desire a human to human connection and want to communicate with an empathic human rather than a bot or an algorithm. The year ahead will be a pivotal milestone for marketers and brands, the ones that use their data to better understand consumer behavior and leverage it to create more personalized, human connections will succeed, while the ones that do not will risk losing loyal customers.

Tirena Dingeldein, Research Director, Capterra

In 2018, if you’re a marketing professional that listened to recommendations of marketing experts everywhere, you collected a lot of customer data and used it to formulate campaigns. The problem of moving marketing forward into 2019 is two-fold; security and recognizing changes in data before solid patterns are defined. Data security, obviously, will be most important for maintaining trust between marketing and their audience, whereas recognizing emerging patterns in the data deluge will mean the difference between cutting-edge marketing or just ‘catching up’ marketing in the new year.

Sid Bharath, growth marketing consultant for tech startups

The biggest challenge with marketing data is figuring out what signals to pay attention to and how to prioritize them. With an explosion of data, the bottleneck moves to how fast you can execute on what the data tells you, and unless you have unlimited resources, you need to prioritize them.

Kent Lewis, Anvil Media

The biggest challenge with marketing data in the coming year will be gaining actionable insight from a flood of data generated via a diverse and numerous set of online (and offline) channels, including social media, website, email, events, PR and advertising.

Daniel Raskin, CMO, Kinetica

The Marketing Data Scientist will be focused on deriving detailed insight about customer behavior and producing reliable predictive and prescriptive insights based on complex data models and machine learning. These models will evolve from historical analysis into real-time applications that transform how products are delivered to customers.

Gennady Gomez, Director of Digital Marketing, Eightfive PR

As marketing data becomes not only more accessible but also much more bountiful, there will be an exponential increase in analysis paralysis. As a result, we’ll start to see the focus in martech shift from data mining to insights reporting, driven by data science and machine learning. These new breed of tools will be critical for marketers as they sort through, identify, and filter actionable data.

Jordan Bishop, Partner, Storied Agency

Until our ability to glean insights from all this data catches up to our ability to capture it, we’ll face the same issue as a city with plenty of cars and not enough roads: traffic. Don’t confuse having more data with having more insights.

Marketers need: Unified data

John Hernandez, CEO, Selligent Marketing Cloud

The biggest data-related challenge will be consolidation and a full 360-degree view of the customer relationship. As it stands, data lives in different places — sales, customer service, digital marketing — and migrating it into a single platform and making sense of it all is going to be difficult. I hope that in a year’s time, we’ll see a lot of progress and proof that leveraging data to focus on delivering personalized, more relevant experiences is the optimum path for better engagement, stronger sales pipelines, and more meaningful marketing results.

Latane Conant, CMO, 6sense

Emerging technology has improved marketing strategy, but the challenge marketers are facing is the daunting task of managing a large number of applications. In the next year, more CMOs need to take a platform approach. Investing in a platform that can be integrated into an existing CRM allows organizations to easily unify their revenue teams, and with the addition of AI incorporated into the platform, unified teams have insight into the behavior of modern buyers with the use of real-time data.

Meisha Bochicchio, PlanSource

Connecting the dots between marketing touch points and giving proper attribution has been and will remain a major challenge for marketers in 2019 … it can be hard to get a full 360-degree view of the true marketing and sales funnel … it is still nearly impossible to combine data from multiple touch points … to paint a full picture of marketing efforts and sales results.

Dietmar Rietsch, CEO, Pimcore

Many marketers have so much data from multiple domains on hand, but no way to streamline and manage it in one centralized location to gain valuable insights.

Eric Quanstrom, CMO, Cience

The biggest issue with marketing data is federating it into a meaningful whole picture. As CMO, I live in (literally) a dozen different dashboards, daily. And that number is growing.

Daniel Jaye, Founder, Aqfer

2019 will be the year enterprises discover that serverless data lakes are a thing, and that they inevitably become data swamps due to widespread data proliferation, chaotic file partitioning/sharding practices, and the lack of traditional data management tools. As marketers are still floundering to piece together the data and figure out whether or not campaigns truly succeeded—they will realize that they can’t keep their heads in the sand on data any longer, and must work to get a better grasp on data management in order to get to the truth about their customers.

Kelly Boyer Sagert, Dagmar Marketing

The biggest challenge will be how to tie all the data together to clearly identify what marketing channels are working or not working. There are multiple touch points to a buyer’s journey and it’s very common to see multiple marketing channels involved in the buyer’s decision, which makes it hard for analytics tools to attribute accurately what marketing channel contributed the most.

Amanda Romano, Twenty Over Ten

The biggest challenge in 2019 will be the ability to bring together … multiple sources of data to connect the dots, make informed decisions and act quickly on those insights.

Aman Naimat, CTO, Demandbase

The marketing technology landscape is increasingly fragmented and that’s not going to slow down. But marketers will need to find a solution to stop isolated data sources from negatively impacting their marketing capabilities in 2019. By integrating key marketing technologies such as CRM, marketing automation and ABM platforms, marketers can start to share data across these applications and get the complete customer view that they crave.

Rebecca Mahoney, CMO, MiQ

Businesses have a wealth of valuable marketing data available to them, but complications arise when this data remains in siloes pertaining to the different departments within that business. This prevents the data from being complementary, and businesses cannot detect potential weak links or new opportunities. With a holistic view of data, powered by marketing intelligence, campaign performance will drastically improve, and otherwise unidentified business opportunities will become unlocked.

Brian Czarny, CMO, Factual

In 2019, marketers will be faced with the challenge of data implementation. Marketers know how valuable data is, but struggle to make sense of it as they’re faced with the challenge of navigating numerous fragmented platforms and systems to get accurate and quality data. The goal is to gather data from multiple sources that work together to achieve optimum success, but there isn’t one standard way to streamline data. Eventually, unlocking this will give marketers the capability to improve context, relevance, and develop creative that resonates.

Eric Keating, VP Marketing, Zaius

The key is to centralize … data and connect every interaction to a single customer ID. Then you can actually understand how your customer behaves across channels and devices. But even more importantly, that data has to connect to your marketing execution platforms directly, so you actually use those insights to power your marketing.

Marketers need: AI and machine learning

Prasad Chalasani, Chief Scientist, MediaMath

The increase and abundance of data that is available now due to integrated marketing platforms will demonstrate the flaws in various deep learning models. Deep learning models have been shown to be vulnerable to imperceptible perturbations in data, that dupe models into making wrong predictions or classifications. With the growing reliance on large datasets, AI systems will need to guard against such attacks on data, and the savviest advertisers will increasingly look into adversarial ML techniques to train models to be robust against such attacks.

Pini Yakuel, CEO and Founder, Optimove

Marketers are equipped with more consumer data than ever before, which can give them valuable insights into their customer base. Many are eager to use AI to automate and personalize communications, but lack the proper infrastructure and data know-how for AI to work properly. There are countless marketing AI platforms available, but until brands are able to properly segment their datasets and make their data truly work for them, they won’t have the ability to conduct innately intelligent marketing. What does this kind of marketing look like? All the marketer needs to do is set the framework, and the AI takes it from there to create personalized messaging for consumers. In 2019, we can expect to see a push from brands to organize their data within a framework that allows them to hyper-personalize communications.

Patricia Nagle: Senior Vice President, CMO, OpenText

Analytics continue to be a critical way to review the impact of marketing on business objectives. In 2019 the continued adoption of dashboard reporting and analysis systems will improve how we measure marketing programs and tactics. With better understanding of all marketing functions, organizations can take a more strategic approach and focus on what’s performing best. We also believe marketing and customer engagement will be an excellent first use-case for enterprise AI. AI systems can analyse structured and unstructured data to identify opportunities for marketing outreach, customer support, and other actions that enhance overall customer experience.

Jonathan Poston, Director, Tombras

We are collecting and analyzing real time data using AI powered platforms … we are telling the story, giving shape and voice to the billions of data points that would otherwise be a bottomless inkwell of unrealized potential.

Brandon Andersen, Chief Strategist, Ceralytics

Marketing data and insights [will become] cheaper due to marketing AI platforms. It will no longer take big budgets, multiple data vendors, and a team of analysts to get actionable insights.

Marketers need: Data security, consumer trust, privacy

Lloyd Adams, SVP, SAP North America

Data ethics will become more important than data analytics.

Jeremiah Owyang, Jessica Groopman, Jaimy Szymanski & Rebecca Lieb, Kaleido Insights

In 2019, marketers will struggle with the social contract of data exchange between consumers and brands. They’ll wrestle with these questions: How will users be compensated beyond personalization? How can marketers do this without being “creepy”? And, as more biometric data emerges, how can marketers use in an ethical manner?

Augie Ray, Director, Gartner

Making better and more critical decisions about what to collect, how to protect it, how to combine it, and how to use it. The idea of a “360-degree view of the customer” has encouraged brands to collect as much data as possible, but this should never be a goal because it raises risks such as privacy concerns, data breaches, GDPR compliance, and customer distrust. Marketers and customer experience leaders need to focus on prioritizing their data needs, better assessing risks, and developing a data strategy that prioritizes and centers on what data is essential and how it will be used over accumulating more of it.

Penny Wilson, CMO, Hootsuite

The single biggest challenge B2B marketers face in the coming year will be balancing privacy and personalization to regain the trust of their audiences. In many ways, 2018 was a tumultuous year for brands, marketers, and customer experience leaders. Concerns around fake news, fake followers, and data privacy led individuals to question their trust in politicians, media outlets, social networks, and businesses alike. Those same concerns extended to how brands — both B2C and B2B — forge relationships with customers, and the data they use to do so. The priority for B2B marketers in 2019 must be to reassure customers – and their customers’ customers – their data is safe and secure. This has to be achieved in the changing climate of customer expectations. Increasingly customers — be they businesses or individuals — are expecting content that is important, interesting and timely to them. That starts with respecting their privacy, being open and transparent about when and why data is collected, and then leveraging the data that customers are willing to share to create personalized 1:1 experiences that deliver unique value. Going forward, brands must focus less on maximizing reach, and more on generating transparent, quality engagements that add value to their customers.

Mike Herrick, SVP, Urban Airship

The biggest challenge marketers will face in 2019 is activating their first party data and growing its use. Brands are facing a privacy paradox as customers increasingly expect personalized service, but both data regulations and consumer privacy controls whittle away at third-party data and tracking. To remain compliant and provide great customer experiences, brands will increasingly rely on data customers willingly provide in the course of direct interactions across engagement channels–websites, apps, messages, social and in-store interactions and more. The best brands will go beyond gaining opt-ins, subscriptions and followers, and use these interactions to collect context and content preferences for each individual.

Tifenn Dano Kwan, CMO, SAP Ariba

A greater focus needs to be on being data compliant as well as on the ease of leveraging data.

Kedar Deshpande, VP, Zappos

The biggest challenge with marketing data stems from the fact that marketers have so much data available to them today, which they’re able to use to reach customers in a very precise way, yet there’s currently a huge lack of communication between brands and customers as to how and why that data is being used. Without more transparency with customers around why personalized outreach is happening and how it’s benefiting them, the immediate reaction is one of distrust, uncertainty, and even fear or anger. In 2019, brands need to focus on clear messaging that explains to customers why they’re using personalization tactics, how their privacy is protected, and what they stand to gain from it.

Briana Brownell, CEO, PureStrategy

With ad-blockers becoming ubiquitous and privacy concerns reaching a fever pitch, marketers need to rebuild the trust that has been lost with consumers. Some of the most successful marketing campaigns in recent times have been honest and authentic, sometimes to the point of distress: KFC’s apology for running out of chicken and Nike’s partnership with controversial quarterback Colin Kaepernick. The biggest challenge in 2019 and beyond will be to create a new normal between marketers and consumers.

Len Shneyder, VP, SendGrid

May 25, 2019 will mark 1 year since GDPR came into force in the EU. This privacy law sets concrete standards on how EU citizen data has to be treated in addition to strict guidelines on consent. Successful senders will have taken stock of this law and enacted internal processes to ensure compliance with European law.

Ben Plomion, CMO, GumGum

As data protection regulations like GDPR become increasingly prevalent in 2019, marketers will struggle to target customers with individually customized online advertising. The most successful marketers will be those who can deliver individualized experiences, without individual user data. Computer vision and other contextual analysis technologies will be necessary to anonymously align ads with the likeliest potential customers.

Esteban Contreras, Senior Director, Hootsuite

We have more data than ever before in history and that in itself is a challenge. One of the most important problems to overcome is how to effectively handle and leverage data – data engineering, data analysis and data science – with legitimate consumer empathy. We need to consider privacy by design. The ethical use of big and small data is ultimately about creating value (e.g. personalizing and contextualizing experiences) without misleading or dehumanizing anyone.

Marketers need: Less data (or at least, smarter data)

Alyssa Hanson, Intouch Insight

The biggest challenge with marketing data will be having too much of it, specifically for B2C organizations.

Marketers crave access to information, but we’re drowning in a virtual quagmire of data. We want to get granular, digging deeper into every data point, but we’re stuck with analysis paralysis — unable to prioritize actions that will have the greatest impact on our KPIs.

Marketers will need a cutting-edge customer experience platform that recommend strategic actions tied to specific KPIs.

Hillel Fuld, Strategic Advisor

Noise. There is a lot of it and it is increasing exponentially. As data volumes increase, the tools we will need to filter it all and extract the valuable components will have to increase in their abilities accordingly.

Tom Bennet, Head of Analytics, Builtvisible

The sheer volume of data being collected is itself posing a challenge to marketers. As we move into 2019, the ability to separate out meaningful patterns and relevant trends from the vast quantities of background noise will be a real test for many marketing teams.

Neil Callanan, Founder, MeetBrief

Because there’s just more and more data with disparate KPIs coming from different sources, the biggest challenge will be in deciding what to ignore and what to value.

Alicia Ward, Flauk

I predict a big challenge for marketers in 2019 will be remembering to use data as only part of the story and keep an eye on the bigger picture. How many times have any of us been hit with poor targeting because of something else we’ve liked or clicked on? It will be important for marketers to remember that the people they are marketing to are real, complex people who should be considered as more than just a few data points.

Matt Hogan, Head of Customer Success, Intricately

The biggest challenge for marketers is focusing on the data that matters. There is a lot of noise out there and each team member needs to know which data is significant to their success. But, it’s not just about having the data but putting it into context to make strategic revenue-driving business decisions. If you aren’t able to execute on your marketing data, it is useless.

Zachary Weiner, CEO, Emerging Insider

I think the core problem moving into 2019 where marketing data is concerned is that with ever increasing amounts of data, marketers are still looking at each marketing silo individually or in small affiliated clusters as opposed to cross-analyzing insights across the entire marketing, sales and customer service value-chain. Often silo based marketing teams will look individually at social data, demand-gen, PR, sales and customer service rather than studying where they intersect and interact. This has always been faulty and is continuing to be a greater problem as each and every silo continues to yield more data.

Jo Ann Sanders, VP of Product Marketing, Optimizely

The biggest challenge marketers will face in 2019 is getting access to the right data to know definitively what their digital users want. Marketers are drowning in data from various analytics systems that provide a historical view of the past. They then ideate ways to improve based on this past data, spend resources to deploy updates, and then re-measure to see if their ideas worked. This process of guessing at what will improve conversion metrics can take weeks or months.

What marketers are going to have to do going forward to succeed so they can keep pace with rapid innovation is to go beyond analytics data that tells them where they have been and adopt new, agile test and learn practices. This will take the guesswork out of what users want, and better ensure that they are rolling out winning user experiences quickly.

With the proliferation of marketing data the challenge will be how to use the latest tools to narrow 1000 points of data down into the few key quality ones that are needed to improve business operations and to better communicate with your customer.

David Meiselman, CMO, exCater

In many ways, marketing has too much data on its hands. The challenge is to figure out which data helps the most to optimize targeting, messaging, and conversions. Traditionally, marketers would work their way through countless A-B tests to determine what works. Today, machine learning and artificial intelligence is helping us accelerate that process to detect correlations and causal factors to improve marketing outcomes across the board, from which people to target for highest conversion to what message to send when for greatest effect.

Tim Minahan, CMO, Citrix

The problem is, we’ve put too many tools in place to collect and analyze marketing data that are too hard to use and it’s causing a lot of frustration. Marketing professionals are spending way too much time searching for information and clicking through multiple pages in applications to gather the insights they need to design, execute, and measure effective campaigns.

Matt Buder Shapiro, Founder & CMO, MedPilot

For many years we’ve been trying accumulate as much data as possible, and we’re now ironically in a difficult position of potentially having too much data. We need to remember to sit back and discover what is actually happening at different points in time, so that we can figure out how it all fits together. We also can never forget that the most important data point when building attribution is still “Where did you hear about us?”

Marketers need: Many more things

These quotes don’t fit an exact category. But they’re too good to not use.

Jenni Schaub, Strategic Planning Director, DEG Digital

But always still remember, a mountain of data is not a replacement for empathy

Stephanie Smith, Co-Founder and President, MOJO PSG

Thoughtfully exploring and formulating the question you’re actually trying to use data to answer is a key challenge that marketers must face in 2019. Without taking the time to define the problem we’re solving for, we end up wasting a lot of time swimming in seas of data and even potentially misusing the data we uncover. Marketers must also find a balance between using data to inform, rather than dictate, decisions, as the marketing craft will always be a blend of art and science.

Scott Gifis, President of AdRoll, AdRoll Group

Measurement is hard. For SMBs and mid-market companies, it is harder, and the stakes are often higher. Although last click measurement is an archaic way to measure performance and impact, many marketers still rely on it because they don’t see accessible alternatives, as sophisticated tools are often difficult to set up or not flexible enough to work within their data model.. Yet, marketers are hungry for change and searching for a better way to provide visibility and optimize their campaigns. I see 2019 as the year modern marketers stop relying on vanity metrics and outdated measurement models and start looking at what is actually driving sales. Further, marketers need to embrace multi-channel adoption and prioritize creating connected stories across all touchpoints.

Norman Guadagno, Head of Marketing, Carbonite

The biggest challenge for marketers will be navigating the evolution of what marketing is in a post-truth world. In 2019, marketers will need to ask themselves the difference between truth and propaganda.

Summary

Thanks to all the marketers who participated in this research, which was initially for my column in Inc, but grew beyond that.

Clearly, there’s a significant change in marketing data policy coming. Marketers know that it’s not about quantity of data but quality. They also know that insights on next best actions is the thing they need their data to reveal. And they are more than cognizant now that consumer privacy matters, and companies that violate their customers’ and prospects’ trust do get punished, both financially and in reputation.

We have to give the last word to Jolene Rheaulot from The Bid Lab.

She said this, which every marketer should remember:

The biggest challenge with the breadth of marketing data available to a company is to keep the data human.

. . .

. . .

Looking for a platform to unify your marketing data and derive smart insights on how to grow?

Get a demo and see if Singular is for you.

Why you’re losing 50% of your ad effectiveness if you’re not using creative reporting

What really makes ads work?

This simple question is the billion-dollar puzzle that drives the adtech industry. For marketers, finding the answer unlocks the door to optimizing growth.

– who you send your ads to matters
– where people see them matters
– how often people see your ads matter
– the brand attached to them matters

But creative outweighs them all. And not by a little. Combined.

Why creative is so overwhelmingly important

All of these things are important, naturally. But your advertising effectiveness is mostly determined by one critical quality: the creative.

According to Nielsen, the quality, messaging, and context of your creative is responsible for as much as 49% of all sales lift. How many people see your ads is just 22%. Targeting to the right kinds of people? Only 9%.

Why?

Creative is emotionally powerful.

In fact, a study published in the Journal of Advertising found that ad creativity impacts 13 key variables in five separate stages of the ad experience, from brand awareness to liking, accepting/rejecting claims, and future brand intentions. For 12 of those 13 variables, great creative drives positive impact, and poor creative gets ignored.

According to Ipsos, a massive 75% of an ad’s ability to make a brand impression is due to creative. Creative is so important that ads that win awards, Ipsos says, generate a full eleven times more share growth.

It’s hard to overstate the importance of this finding.

If you succeed at everything else in advertising, but fail in creative, you are leaving almost 50% of your results on the table according to Nielsen, and 75% of your potential results on the table according to Ipsos.

That is why you need creative reporting

Singular offers creative reporting because it’s so critical. It’s something you can get in many places, of course, in silos. Facebook, for instance, offers creative reporting that tells you what images performed well on its platform.

That’s great.

But what top marketers need is an understanding of how their creative is performing across all their ad partners.

One major Singular customer, for instance, works with 20+ different channels including Facebook, Snapchat, Instagram, Twitter, Pinterest, and Google at any given time. In each, this client is using between 15 and 30 different creative units.

Yep.

That’s between 350 and 600 different combinations of platform and creative at any given moment.

That’s not just mildly challenging to measure as a marketer. It’s basically impossible without automated help. The problem is that you don’t know which creative might resonate with which audience.

But you absolutely need to.

Some images will work well in one context and bomb in another. Some videos will resonate with the unique demographic slice that ad partner A accesses, and achieve a collective yawn from ad partners B’s audience. And a playable ad that hits one ad network audience’s behavior graph may not touch another.

Creative reporting is the solution.

“Singular’s Creative Reporting determines asset level ROI across more media sources than any other provider,” says Singular senior product marketing manager Saadi Muslu. “With it, you can quickly identify creative performance against any dimension and metric, group similar creatives regardless of minor copy or compression differences, or group creatives by keyword using tags based on any dimension.”

Why we sometimes ignore creative: piping and wiring

There is a lot of infrastructure in the modern marketing department.

The data explosion and almost 7,000 marketing technology tools haven’t helped with this, and the large numbers of ad networks and partners we work with add to it. In fact — and it’s something we’ll be releasing data on next month — Singular data indicates that top-performing marketers work with a much wider variety of ad partners than average or poor-performing marketers.

(Watch for that report soon!)

But there’s another challenge to all this martech/adtech piping and wiring.

Sometimes it’s easier to focus on the pipes and the wires than on what they’re actually carrying. The world of marketing technology seems concrete, observable, and controllable. If we create a drip email flow, we can set it up, schedule it, and press save. If we’re initiating an ad campaign, we set parameters, initiate buys, and monitor performance.

Creative doesn’t work that way (although AI is getting better at helping).

There’s no button to press for great copy, compelling images, or a funny video. It’s not linear, doesn’t follow a defined process, and can’t be switched on and off.

Magic: melding art and science, data and creativity

That’s where the magic enters, however.

When we pair marketing designers’ and writers’ creativity with insights from marketing data — like those in Singular’s Creative Reporting — we can set creativity free to try dozens of different things, and let data decide which resonate, which penetrate, and which generate productive results.

Nothing could be simpler, even at scale.

Another of Singular’s clients builds an astonishing 50 videos each and every week. Pairing that level of creativity with the data that indicates which ones work would be a tough task, manually. But letting machines do what machines do well makes it possible.

And, tells marketers once and for all: what makes their ads work.

Next step: learn more about Creative Reporting.

Using attribution data to calculate mobile ads LTV

Eric Benjamin Seufert is the owner of Mobile Dev Memo, a popular mobile advertising trade blog. He also runs Platform and Publishing efforts at N3TWORK, a mobile gaming company based in San Francisco, and published Freemium Economics, a book about the freemium business model. You can follow Eric on Twitter.

Note: if you’re looking for ad monetization with perhaps less effort than Eric’s method below, talk to your Singular customer service representative (and stay tuned for additional announcements).

Various macro market forces have aligned over the past two years to create the commercial opportunity for app developers to generate significant revenue from in-app advertising. New genres like hypercasual games and even legacy gaming genres and non-gaming genres have created large businesses out of serving rich media video and playable ads to their users by building deep, sophisticated monetization loops that enrich the user experience and produce far less usability friction than some in-app purchases.

But unfortunately, while talented, analytical product designers are able to increase ad revenues with in-game data by deconstructing player behavior and optimizing the placement of ads, user acquisition managers have less data at their disposal in optimizing the acquisition funnel for this type of monetization. Building an acquisition pipeline around in-app ads monetization is challenging because many of the inputs needed to create an LTV model for in-app ads are unavailable or obfuscated. This is evidenced in the fact that a Google search for “mobile app LTV model” yields hundreds of results across a broad range of statistical rigor, but a search for “mobile app ads LTV model” yields almost nothing helpful.

Why is mobile ads LTV so difficult to calculate?

For one, the immediate revenue impact of an ad click within an app isn’t knowable on the part of the developer and is largely outside of their control. Developers get eCPM data from their ad network partners on a monthly basis when they are paid by them, but they can’t really know what any given click is worth because of the way eCPMs are derived (ad networks usually get paid for app installs, not for impressions, so eCPM is a synthetic metric).

Secondly, app developers can’t track ad clicks within their apps, only impressions. So while a developer might understand which users see the most ads in their app and can aggregate that data into average ad views per day (potentially split by source), since most ad revenue is driven by the subsequent installs that happen after a user clicks on an ad, ad view counts alone don’t help to contribute to an understanding of ads LTV.

Thirdly, for most developers, to borrow conceptually from IAP monetization, there are multiple “stores” from which ad viewing (and hopefully, clicking) users can “purchase” from: each of the networks that an app developer is running ads from, versus the single App Store or Google Play Store from which the developer gathers information. So not only is it more onerous to consolidate revenue data for ads, it also further muddies the monetization waters because even if CPMs for various networks can be cast forward to impute revenue, there’s no certainty around what the impression makeup will look like in an app in a given country on a go-forward basis (in other words: just because Network X served 50% of my ads in the US this month, I have no idea if it will serve 50% of my ads in the US next month).

For digging into problems that contain multiple unknown, variable inputs, I often start from the standpoint of: If I knew everything, how would I solve this? For building an ads LTV model, a very broad, conceptual calculation might look like:

What this means is: for a given user who was acquired via Channel A, is using Platform B, and lives in Geography C, the lifetime ad revenue they are expected to generate is the sum of the Monthly Ad Views we estimate for users of that profile (eg. Channel A, Platform B, Geography C) times the monthly blended CPM of ad impressions served to users of that profile.

In this equation, using user attribution data of the form that Singular provides alongside internal behavioral data, we can come up with Lifetime Ad Views broken down by acquisition channel, platform, and geography pretty easily: this is more or less a simple dimensionalized cumulative ad views curve over time that’d be derived in the same way as a cumulative IAP revenue curve.

But the Blended CPM component of this equation is very messy. This is because:

  • Ad networks don’t communicate CPMs by user, only at the geo level; [Editorial note: there is some significant change happening here; we will keep you posted on new developments.]
  • Most developers run many networks in their mediation mix, and that mix changes month-over-month;
  • Impression, click, and video completion counts can be calculated at the user level via mediation services like Tapdaq and ironSource, but as of now those counts don’t come with revenue data.

Note that in the medium-term future, many of the above issues with data availability and transparency will be ameliorated by in-app header bidding (for a good read on that topic, see this article by Dom Bracher of Tapdaq). In the meantime, there are some steps we can take to back into reasonable estimates of blended CPMs for the level of granularity that our attribution data gives us and which is valuable for the purposes of user acquisition (read: provides an LTV that can be bid against on user acquisition channels).

But until that manifests, user acquisition managers are left with some gaps in the data they can use to construct ads LTV estimates. The first glaring gap is the network composition of the impression pool: assuming a diverse mediation pool, there’s no way to know which networks will be filling what percentage of overall impressions in the next month. And the second is the CPMs that will be achieved across those networks on a forward-looking basis, since that’s almost entirely dependent on whether users install apps from the ads they view.

The only way to get around these two gaps is to lean on historical data as a hint at what the future will look like (which violates a key rule of value investing but is nonetheless helpful in forming a view of what’s to come). In this case, we want to look at past CPM performance and past network impression composition for guidance on what to expect on any given future month.

Estimating mobile ads LTV in Python

To showcase how to do that, we can build a simple script in python, starting with the generation of some random sample data. This data considers an app that is only serving ads to users from Facebook, Unity, and Vungle in the US, Canada, and UK:

[code]
import pandas as pd
import matplotlib
import numpy as np
from itertools import product
import random

geos = [ 'US', 'CA', 'UK' ]
platforms = [ 'iOS', 'Android' ]
networks = [ 'Facebook', 'Unity', 'Applovin' ]

def create_historical_ad_network_data( geos, networks ):
 history = pd.DataFrame(list(product(geos, platforms, networks)),
 columns=[ 'geo', 'platform', 'network' ])

 for i in range( 1, 4 ):
 history[ 'cpm-' + str( i ) ] = np.random.randint ( 1, 10, size=len( history ) )
 history[ 'imp-' + str( i ) ] = np.random.randint( 100, 1000, size=len( history ) )
 history[ 'imp-share-' + str( i ) ] = history[ 'imp-' + str( i ) ] / history[ 'imp-' + str( i ) ].sum()

 return history

history = create_historical_data(geos, networks)
print(history)
[/code]

Running this code generates a Pandas DataFrame that looks something like this (your numbers will vary as they’re randomly generated):

[code / table]
geo platform network cpm-1 imp-1 imp-share-1 cpm-2 imp-2 \
0 US iOS Facebook 2 729 0.070374 9 549 
1 US iOS Unity 7 914 0.088232 3 203 
2 US iOS Applovin 7 826 0.079737 4 100 
3 US Android Facebook 2 271 0.026161 2 128 
4 US Android Unity 5 121 0.011681 9 240 
5 US Android Applovin 6 922 0.089005 9 784 
6 CA iOS Facebook 2 831 0.080220 9 889 
7 CA iOS Unity 8 483 0.046626 5 876 
8 CA iOS Applovin 7 236 0.022782 9 642 
9 CA Android Facebook 8 486 0.046916 4 523 
10 CA Android Unity 1 371 0.035814 5 639 
11 CA Android Applovin 8 588 0.056762 7 339 
12 UK iOS Facebook 2 850 0.082054 8 680 
13 UK iOS Unity 7 409 0.039483 3 310 
14 UK iOS Applovin 1 291 0.028092 5 471 
15 UK Android Facebook 7 370 0.035718 6 381 
16 UK Android Unity 3 707 0.068250 6 117 
17 UK Android Applovin 3 954 0.092094 3 581

imp-share-2 cpm-3 imp-3 imp-share-3 
0 0.064955 8 980 0.104433 
1 0.024018 4 417 0.044437 
2 0.011832 3 157 0.016731 
3 0.015144 7 686 0.073103 
4 0.028396 3 550 0.058610 
5 0.092759 8 103 0.010976 
6 0.105182 1 539 0.057438 
7 0.103644 6 679 0.072357 
8 0.075958 5 883 0.094096 
9 0.061879 1 212 0.022592 
10 0.075603 8 775 0.082587 
11 0.040109 6 378 0.040281 
12 0.080454 6 622 0.066283 
13 0.036678 8 402 0.042839 
14 0.055726 7 182 0.019395 
15 0.045078 2 623 0.066390 
16 0.013843 2 842 0.089727 
17 0.068741 1 354 0.037724
[/code]

One thing to consider at this point is that we have to assume, on a month-to-month basis, that any user in any given country will be exposed to the same network composition as any other user on the same platform (that is, the ratio of Applovin ads being served to users in the US on iOS is the same for all users of an app in a given month). This almost certainly isn’t strictly true, as, for any given impression, the type of device a user is on (eg. iPhone XS Max vs. iPhone 6) and other user-specific information will influence which network fills an impression. But in general, this assumption is probably safe enough to employ in the model.

Another thing to point out is that retention is captured in the Monthly Ad Views estimate that is tied to source channel. One common confusion in building an Ads LTV model is that there are ad networks involved in both sides of the funnel: the network a user is acquired from and the network a user monetizes with via ads served in the app. In the construction of our model, we capture “user quality” in the Monthly Ad Views component from Part A, which encompasses retention in the same way that a traditional IAP-based LTV curve does. So there’s no reason to include “user quality” in the Part B of the equation, since it’s already used to inform Part A.

Given this, the next step in approximating Part B is to get a historical share of each network, aggregated at the level of the Geo and Platform. Once we have this, we can generate a blended CPM value at the level of Geo and Platform to multiply against the formulation in Part A (again, since we assume all users see the same network blend of ads, we don’t have to further aggregate the network share by the user’s source network).

In the below code, the trailing three-month impressions are calculated as a share of the total at the level of Geo and Platform. Then, each network’s CPM is averaged over the trailing three months and the sumproduct is returned:

[code]
history[ 'trailing-3-month-imp' ] = history[ 'imp-1' ] + history[ 'imp-2' ] + history[ 'imp-3' ]

history[ 'trailing-3-month-imp-share' ] = history[ 'trailing-3-month-imp' ] / history.groupby( [ 'geo', 'platform' ] )[ 'trailing-3-month-imp' ].transform( sum )

history[ 'trailing-3-month-cpm' ] = history[ [ 'cpm-1', 'cpm-2', 'cpm-3' ] ].mean( axis=1 )

blended_cpms = ( history[ [ 'trailing-3-month-imp-share', 'trailing-3-month-cpm' ] ].prod( axis=1 )
 .groupby( [ history[ 'geo' ], history[ 'platform' ] ] ).sum( ).reset_index( )
)

blended_cpms.rename( columns = { blended_cpms.columns[ len( blended_cpms.columns ) - 1 ]: 'CPM' }, inplace = True )

print( blended_cpms )
[/code]

Running this snippet of code should output a DataFrame that looks something like this (again, the numbers will be different):

[code]
geo platform CPM
0 CA Android 5.406508
1 CA iOS 4.883667
2 UK Android 4.590680
3 UK iOS 5.265561
4 US Android 4.289083
5 US iOS 4.103224
[/code]

So now what do we have? We have a matrix of blended CPMs broken out at the level of Geo and Platform (eg. the CPM that Unity Ads provides for US, iOS users) — this is Part B from the equation above. The Part A from that equation — which is the average number of ad views in a given month that we expect from users that match various profile characteristics pertaining to their source channel, geography, and platform — would have been taken from internal attribution data mixed with internal app data, but we can generate some random data to match what it might look like with this function:

[code]
def create_historical_one_month_ad_views( geos, networks ):
 ad_views = pd.DataFrame( list( product( geos, platforms, networks ) ), 
 columns=[ 'geo', 'platform', 'source_channel' ] )
 ad_views[ 'ad_views' ] = np.random.randint( 50, 500, size=len( ad_views ) )
 
 return ad_views

month_1_ad_views = create_historical_one_month_ad_views( geos, networks )
print( month_1_ad_views )
[/code]

Running the above snippet should output something like the following:

[code]
geo platform source_channel ad_views
0 US iOS Facebook 73
1 US iOS Unity 463
2 US iOS Applovin 52
3 US Android Facebook 60
4 US Android Unity 442
5 US Android Applovin 349
6 CA iOS Facebook 279
7 CA iOS Unity 478
8 CA iOS Applovin 77
9 CA Android Facebook 479
10 CA Android Unity 120
11 CA Android Applovin 417
12 UK iOS Facebook 243
13 UK iOS Unity 306
14 UK iOS Applovin 52
15 UK Android Facebook 243
16 UK Android Unity 106
17 UK Android Applovin 195
[/code]

We can now match the performance data from our user base (gleaned using attribution data) with our projected CPM data to get an estimate of ad revenue for the given month with this code:

[code]
combined = pd.merge( month_1_ad_views, blended_cpms, on=[ 'geo', 'platform' ] )
combined[ 'month_1_ARPU' ] = combined[ 'CPM' ] * ( combined[ 'ad_views' ] / 1000 )

print( combined )
[/code]

Running the above snippet should output something like the following:

[code]
geo platform source_channel ad_views CPM month_1_ARPU
0 US iOS Facebook 73 5.832458 0.425769
1 US iOS Unity 463 5.832458 2.700428
2 US iOS Applovin 52 5.832458 0.303288
3 US Android Facebook 60 5.327445 0.319647
4 US Android Unity 442 5.327445 2.354731
5 US Android Applovin 349 5.327445 1.859278
6 CA iOS Facebook 279 6.547197 1.826668
7 CA iOS Unity 478 6.547197 3.129560
8 CA iOS Applovin 77 6.547197 0.504134
9 CA Android Facebook 479 4.108413 1.967930
10 CA Android Unity 120 4.108413 0.493010
11 CA Android Applovin 417 4.108413 1.713208
12 UK iOS Facebook 243 4.626163 1.124158
13 UK iOS Unity 306 4.626163 1.415606
14 UK iOS Applovin 52 4.626163 0.240560
15 UK Android Facebook 243 5.584462 1.357024
16 UK Android Unity 106 5.584462 0.591953
17 UK Android Applovin 195 5.584462 1.088970
[/code]

That last column — month_1_ARPU — is the amount of ad revenue you might expect from users in their first month, matched to their source channel, their geography, and their platform. In other words, it is their 30-day LTV.

Putting it all together

Hopefully this article has showcased the fact that, while it’s messy and somewhat convoluted, there does exist a reasonable approach to estimating ads LTV using attribution and ads performance data. Taking this approach further, one might string together more months of ad view performance data to extend the limit of the Ads LTV estimate (to month two, three, four, etc.) and then use historical CPM fluctuations to get a more realistic estimate of where CPMs will be on any given point in the future (for example, using a historical blended average doesn’t make sense in the run-up to Christmas, when CPMs spike).

The opportunities and possibilities for making money via rich ads at this point of the mobile cycle are exciting, but they don’t come without new challenges. In general, with the way the mobile advertising ecosystem is progressing towards algorithm-driven and programmatic campaign management, user acquisition teams need to empower themselves with analytical creativity to find novel ways to scale their apps profitably.

. . .

. . .

Next: Get the full No-BS Guide to Mobile Attribution, for free, today.

Grow faster: How ‘Dual Integration’ unlocks vastly more value than vanilla mobile attribution

Peanut butter is just peanut butter. And chocolate is just chocolate. But if you have the creativity and insight to combine them, you create a magical mystery confection that makes your mouth cry out for joy and high-five your stomach. You get, perhaps, dual integration.

Imagine the peanut butter is marketing campaign data.

Imagine the chocolate is attribution.

Put them together, and the result is not magical and not mysterious: it’s marketing science that unlocks ever-increasing but previously hidden value. And that’s just one of the secrets revealed in our No Bullsh!t Guide to Mobile Attribution.

But what exactly is dual integration? And how does it work?

Dual integration technology

“Simplistically, dual integration technology is connecting marketing data with outcome data,” says Singular VP of Client Services Victor Savath. “On the marketing side, we’re talking about information on campaigns, publisher, creative, and sub-campaigns. On the outcome or attribution side, we’re talking about user or customer install and event data.”

Ultimately, you’re combining spend data with mobile attribution data.

But … at as granular a level as you implement your marketing spend.

That means every outcome, or attribution, is enriched with campaign information. Now you know not only that you acquired a new customer, or user, from Ad Partner XYZ. You also know what campaign it was from. Where the campaign and the customer intersected. And what specific creative cued the conversion.

When you combine these two datasets, you get true granular ROI, says Savath.

“It’s not about whether or not a network performs, it’s what is performing within a particular network,” Savath told me yesterday. “Sometimes we see that marketers are quick to dismiss performance marketing, or a particular ad network, because the results are all blended. But granularity highlights the pockets of value. For example, in one network … one specific set of creative might work very, very well, while another does not. With granularity, you know.”

Alternatively, some publishers or traffic sources that an ad network uses for your campaigns might be horrible: poor quality or even fraudulent. But other traffic sources are amazing. Seeing this close up means that marketers can optimize for the best-performing publishers within an ad network. That unlocks potential pockets of profitable growth.

The problem?

Most marketers aren’t able to get to that point.

Missing out on magic (or marketing science)

There are many different types of granularity: creative, publisher, network, campaign, region, with metrics from both the network and attribution side. But what matters the most is ROI granularity … which is inherently matched to your ability to tie both sides of the equation together.

The problem is that most marketers don’t have a tool that connects and aligns all the data properly.

And that means they’re making future resource allocation decisions based on limited information.

“For example, if you’re just using vanilla attribution data, you might say that a certain publisher is generating revenue for you,” Savath says. “The problem is, you’re not exactly clear at what specific cost you’ve achieved this revenue.”

Dual integration might show you that A, B, and E campaigns are really working well with a certain ad network, while C and D are not: they’re complete duds. That insight may mean the difference between writing off an ad network as a total loss versus optimizing your efforts with that partner.

And, of course, achieving much better results.

The big aggregated campaign picture alone has its own challenges, of course.

“Alternatively, if you’re just using spend data, you don’t understand your outcomes at all,” says Savath.

Magic isn’t hard. It’s science

The best part is that with Singular, dual integration isn’t any integration at all. At least, not on your part.

Singular does it for you. And it’s not a back-end thing, it’s a built-in thing.

Most attribution solutions provide tools to create tracking links, or make them in bulk, or allow marketers to import them. The problem is that most marketing managers build tracking links in a vacuum, without knowledge of how a partner will report spend back to you. With Singular, there’s no manual link building … Singular removes the whole element of manual creation of tracking URLs from the measurement workflow.

“Instead, Singular creates the links for you and automatically embeds campaign, creative, publisher, ad network, and other data into your tracking links,” says Savath. “Since our marketing data is informing what the link structure should be, you have automatic alignment between marketing data and attribution data. And thanks to Singular’s deep integrations to thousands of ad networks and marketing partners, your URLs will always have the right parameters and the right values.”

ROI versus IOR

Thanks to the performance-based nature of much of modern mobile marketing, marketers are not so much calculating return on investment as investment on return. In other words, they get the attributed results of their marketing and determine how spend and marketing activity relates to those results.

While there’s definitely a big place in performance marketing for spending based on results, only being able to look at marketing data this way creates serious challenges.

One of the biggest: data reconciliation problems.

“Singular’s approach is matching conversions to spend versus matching spend to conversions,” says Savath.

Get the full Guide for much more

The full No BullSh!t Guide to Attribution contains much more insight on how to do attribution right, focusing on seven core topics:

  1. Mobile Measurement Partners (MMPs)
  2. Data combining
  3. Granularity
  4. Reporting
  5. Fraud prevention
  6. Data retention & accessibility
  7. Pricing

Get the full guide, for free, today.